The invention relates to the general field of aviation, and in particular to monitoring aeroengines (i.e. aircraft engines), such as turbine engines, for example.
The invention relates more particularly to a method of normalizing a value of an operating parameter of an aeroengine for use when monitoring the engine.
In the aviation industry, it is essential for a manufacture of aeroengines to master the behavior of those engines in order to demonstrate in particular the manufacturer's design and maintenance capabilities. Such mastery over the behavior of each engine relies in general on experts analyzing the values taken by a selection of operating parameters of the engine that are representative of its state at any given instant and that are determined on the basis of measurements provided by sensors for monitoring the engine. By way of example, such operating parameters may comprise the speed of the engine, its temperature, or indeed its pressure. They may also result from grouping together more elaborate indicators that are specific to a particular physical element of the engine or to a logical element for performing a specific task (e.g. starter system, lubrication system), such as details or shapes of a curve representative of the way measurements delivered by one or more engine monitoring sensors vary over time.
A difficulty encountered by experts when analyzing values of such operating parameters stems from the fact that a given engine never operates under exactly the same conditions from one mission to another, or indeed even during a single mission. In other words, the values of the operating parameters analyzed by experts result from measurements acquired under conditions that differ, in particular in terms of the environment or the external context (e.g. external temperature, atmospheric pressure, speed and altitude of the aircraft propelled by the engine, geographical location, weather conditions, etc.), thus making it difficult to compare such values with one another for the purpose in particular of detecting a failure of the engine or behavior that is abnormal. In the description below, the term “exogenous variables” or “context variables” are used to designate the various variables representing the external context and that might have an influence on the operating parameters of the engine.
Unfortunately, continuously monitoring operating parameters in order to take those various external contexts into account cannot be envisaged in practice, in particular for reasons of cost and difficulty of implementation.
In order to mitigate that difficulty, it is known to have recourse to so-called “standardization” methods that seek to reduce all of the operating parameter values under study to a standard environment, in particular an environment that is independent of the conditions under which the measurements are actually acquired by the sensors and from which these values are determined. Numerous standardization methods rely on normalizing the values of the operating parameters, i.e. on transforming operating parameter values so that they are distributed with a normal relationship and thus become comparable with one another.
For each operating parameter, conventional normalization consists in evaluating the mean and the standard deviation of a series of values previously collected for that parameter, then in calculating the difference that exists between the mean and the current value of the operating parameter, and then in dividing this difference by the standard deviation. The value as obtained in this way is a normalized value of the operating parameter. Nevertheless, such normalization is not suitable for handling the ways in which the operating parameter values depend on the above-mentioned exogenous variables.
Document EP 2 376 988 describes a method of normalizing a set of indicators that are specific to elements of an aeroengine, which method makes it possible to eliminate the dependencies of those indicators on the external context and to handle the stochastic interdependency relationships between the indicators themselves. That method uses a conditional multidimensional regression model that handles all of the indicators simultaneously while also taking account of a set of exogenous variables.
For each indicator, the definition of that model relies on constructing a projection space on the basis of a set of exogenous variables and of a subset comprising all of the indicators with the exception of the indicator under consideration, and then on using a regression technique to project the real value of the indicator in question onto the projection space as constructed in that way. The projection provides an estimate (i.e. a prediction) of the indicator, which estimate is then subtracted from the real value of the indicator in order to provide a normalized value.
The regression model described in Document EP 2 376 988 is relatively robust, since it is defined from a large panel of data (i.e. of indicator values and of exogenous variables), as collected over a plurality of engines of the same type operating in a mode of operation that is normal (i.e. without any problem).
Nevertheless, under certain circumstances, that model can lack accuracy in the sense that it does not make it possible to take account of the differences that can exist between engines, in particular in terms of age, state during the mission under consideration, or indeed other features specific to each engine on being manufactured.